Choose Between ERGM and SAOM
Match a cross-sectional dependence question or longitudinal micro-change question to the model whose assumptions and data requirements fit.
Method sources
By the end of this tutorial
- 1Define ERGM and SAOM model choice from an explicit node, tie, boundary, direction, weight, and observation-window specification.
- 2Apply and audit this procedure: Map each research question to its time structure, use ERGM for conditional cross-sectional tie probabilities and SAOM for modeled between-wave change, check degeneracy or convergence, and compare simulated goodness of fit.
- 3Interpret the result with a sensitivity check and the following evidence boundary: ERGM and SAOM answer different conditional questions and rely on different assumptions; similar coefficient names do not make estimates interchangeable or causal.
SNA
Network specification
Analysis scenario
A research team has one complete advice network from 18 schools and four friendship waves from one school, but initially proposes the same model for both datasets.
Nodes
The complete eligible actor roster for each network, harmonized within but not falsely pooled across the separate research settings.
Ties
Directed advice ties for each cross-sectional school and directed friendship nominations for each wave of the longitudinal school.
Network type
A collection of cross-sectional directed networks plus one four-wave directed longitudinal network, analyzed as distinct designs.
Step-by-step tutorial
Freeze the relational question
Write the decision the analysis must inform, then lock the eligible node roster, tie-generating event, direction, weight, self-tie rule, observation window, and missing-data code. Preserve a read-only source copy and record why this specification represents the stated question.
Checkpoint
A second analyst can reconstruct the same node set and edge table from the written rules without guessing what an absent record means.
Compute the focal structure
Work on a versioned analysis copy and carry out the focal method exactly as specified: Map each research question to its time structure, use ERGM for conditional cross-sectional tie probabilities and SAOM for modeled between-wave change, check degeneracy or convergence, and compare simulated goodness of fit. Save software and package versions, every threshold, normalization, seed, and intermediate count needed to reproduce the result.
Checkpoint
The output is tied to one named data version and includes the denominator, parameter settings, and a reproducible calculation record.
Run a structural sensitivity check
Repeat the analysis under at least one defensible alternative boundary, missingness rule, tie threshold, weight transformation, or model setting. Compare membership and substantive conclusions, not only a single coefficient, and investigate every change large enough to alter a decision.
Checkpoint
The audit states which patterns persist, which actors or groups change classification, and which conclusion depends on an analyst choice.
Report for responsible action
Pair the numerical result with a table or structure-preserving visual, document excluded and missing actors, and explain uncertainty in plain language. Convert the finding into a reversible support question, not an automatic ranking, while stating this boundary: ERGM and SAOM answer different conditional questions and rely on different assumptions; similar coefficient names do not make estimates interchangeable or causal.
Checkpoint
The final note contains the question, specification, result, sensitivity evidence, uncertainty, privacy controls, and a proportionate next step.
Interpret with care
ERGM and SAOM model choice describes a property of the specified relation and network boundary. It does not transfer automatically to another relation, time period, class, platform, or population.
ERGM and SAOM answer different conditional questions and rely on different assumptions; similar coefficient names do not make estimates interchangeable or causal. Compare the result with raw counts, missingness, plausible alternative specifications, and contextual evidence before acting.